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Humans can accurately determine whether the object in hand has slipped or notby visual and tactile perception. However, it is still a challenge for robotsto detect in-hand object slip through visuo-tactile fusion. To address thisissue, a novel visuo-tactile fusion deep neural network is proposed to detectslip, which is a time-dependent continuous action. By using the multi-scaletemporal convolution network (MS-TCN) to extract the temporal features ofvisual and tactile data, the slip can be detected effectively. In this paper, a7-dregree-of-freedom (7-DoF) robot manipulator equipped with a camera and atactile sensor is used for data collection on 50 daily objects with differentshapes, materials, sizes, and weights. Therefore, a dataset is built, where thegrasping data of 40 objects and 10 objects are used for network training andtesting, respectively. The detection accuracy is 96.96% based on the proposedmodel. Also, the proposed model is compared with a visuo-tactile fusion deepneural network (DNN) based on long short-term memory network (LSTM) on thecollected dataset and a public dataset using the GelSight tactile sensor. Theresults demonstrate that the proposed model performs better on both dataset.The proposed model can help robots grasp daily objects reliably. In addition,it can be used in grasping force control, grasping policy generation anddexterous manipulation.

Visuo-Tactile-Based Slip Detection Using A Multi-Scale Temporal Convolution Network